8:00 – 8:30 Registration, speaker check-in and poster setup
8:30 – 8:45 Opening Remarks
8:45 – 10:00 Morning Session 1: Plenary Talk
Professor Anant Madabhushi, Case Western Reserve University
Radiomics, Pathomics and Deep Learning: Implications for Precision Medicine

10:00-10:30 Coffee break
10:30-12:30 Morning Session 2

Session Chair: TBD
· [MLMI-O-1] Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring
Felix Durlak, Michael Wels, Chris Schwemmer, Michael Suhling, Stefan Steidl, and Andreas Maier
· [MLMI-O-2] Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes
Darko Stern, Philipp Kainz, Christian Payer, and Martin Urschler
· [MLMI-O-3] STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion
Yao Xiao, Ajay Gupta, Pina C. Sanelli, Ruogu Fang
· [MLMI-O-4] Indecisive Trees for Classification and Prediction of Knee Osteoarthritis
Luca Minciullo, Paul A. Bromiley, David T. Felson and Timothy F. Cootes
· [MLMI-O-5] Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Dmitry Petrov, Boris Gutman, Shih-Hua (Julie) Yu, Kathryn Alpert, Artemis Zavaliangos-Petropulu, Dmitry Isaev, Jessica Turner, Theo G.M. van Erp, Lei Wang, Lianne Schmaal, Dick Veltman, and Paul M. Thompson

12:30 – 13:30 Lunch & Posters

· [MLMI-P-1] From Large to Small Organ Segmentation in CT using Regional Context
Marie Bieth, Esther Alberts, Markus Schwaiger, Bjoern Menze
· [MLMI-P-2] Motion Corruption Detection in Breast DCE-MRI
Sylvester Chiang, Sharmila Balasingham, Lara Richmond, Belinda Curpen, Mia Skarpathiotakis, Anne Martel
· [MLMI-P-3] Detection and localization of Drosophila egg chambers in microscopy images
Jiri Borovec, Jan Kybic, Rodrigo Nava
· [MLMI-P-4] Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis
Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, and Vahid Tarokh
· [MLMI-P-5] Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer’s Disease
Jorge Samper-Gonzalez, Ninon Burgos, Sabrina Fontanella1, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot
· [MLMI-P-6] Automatic Classification of Proximal Femur Fractures based on Attention Models
Anees Kazi, Shadi Albarqouni, Amelia Jimenez Sanchez, Sonja Kirchhoff, Peter Biberthaler, Nassir Navab, Diana Mateus
· [MLMI-P-7] Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation
Fahdi Kanavati, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, and Ben Glocker
· [MLMI-P-8] Accurate and Consistent Hippocampus Segmentation through Convolutional LSTM and View Ensemble
Yani Chen, Bibo Shi, Zhewei Wang, Tao Sun, Charles D. Smith, and Jundong Liu
· [MLMI-P-9] Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images
Yuru Pei, Yunai Yi, Gengyu Ma, Yuke Guo, Gui Chen, Tianmin Xu, and Hongbin Zha
· [MLMI-P-10] Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis
Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, and Dinggang Shen
· [MLMI-P-12] Efficient Groupwise Registration for Brain MRI by Fast Initialization
Pei Dong, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, and Dinggang Shen
· [MLMI-P-13] Sparse Multi-View Task-centralized Learning for ASD Diagnosis
Jun Wang, Qian Wang, Shitong Wang, Dinggang Shen
· [MLMI-P-14] Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis
Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Dinggang Shen
· [MLMI-P-15] Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data
Alborz Amir-Khalili, Soheil Kianzad, Rafeef Abugharbieh, and Ivan Beschastnikh
· [MLMI-P-16] Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images
Siqi Liu, Donghao Zhang, Yang Song, Hanchuan Peng, and Weidong Cai
· [MLMI-P-17] Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity
Junyi Yan, Yu Meng, Gang Li, Weili Lin, Dazhe Zhao, Dinggang Shen
· [MLMI-P-18] Gradient boosted trees for corrective learning
Baris U. Oguz, Russell T. Shinohara, Paul A. Yushkevich, and Ipek Oguz
· [MLMI-P-19] Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li
· [MLMI-P-20] A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling
Jinquan Sun, Yinghuan Shi, Yang Gao, and Dinggang Shen
· [MLMI-P-21] Localizing Cardiac Structures in Fetal Heart Ultrasound Video
Christopher P. Bridge, Christos Ioannou, and J. Alison Noble
· [MLMI-P-22] Deformable Registration through Learning of Context-Specific Metric Aggregation
Enzo Ferrante, Puneet K Dokania, Rafael Marini, Nikos Paragios
· [MLMI-P-23] Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification
Yang Li, Jingyu Liu, Meilin Luo, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen
· [MLMI-P-24] Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification
Yang Li, Jingyu Liu, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen
· [MLMI-P-25] Novel Effective Connectivity Network Inference for MCI Identification
Yang Li, Hao Yang, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen
· [MLMI-P-26] Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network
Zeju Li, Yuanyuan Wang, Jinhua Yu
· [MLMI-P-27] Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
Giles Tetteh, Markus Rempfler, Claus Zimmer, Bjoern H. Menze
· [MLMI-P-28] Product Space Decompositions for Continuous Representations of Brain Connectivity
Daniel Moyer, Boris A. Gutman, Neda Jahanshad, and Paul M. Thompson
· [MLMI-P-29] Tversky loss function for image segmentation using 3D fully convolutional deep networks
Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, and Ali Gholipour

13:30 – 15:30 Afternoon Session 1
Session Chair: TBD
· [MLMI-O-6] Atlas of Classifiers for Brain MRI Segmentation
Boris Kodner, Shiri Gordon, Jacob Goldberger, Tammy Riklin Raviv
· [MLMI-O-7] Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base
Yuru Pei, Haifang Qin, Gengyu Ma, Yuke Guo, Gui Chen, Tianmin Xu, and Hongbin Zha
· [MLMI-O-8] Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning based Cascade Framework
Dong Nie, Li Wang, Roger Trullo, Jianfu Li, Peng Yuan, James Xia, Dinggang Shen
· [MLMI-O-9] 3D U-net with Multi-Level Deep Supervision: Fully automatic segmentation of Proximal Femur in 3D MR Images
Guodong Zeng, Xin Yang, Jing Li, Lequan Yu, Pheng-Ann Heng, and Guoyan Zheng
· [MLMI-O-10] Whole brain segmentation and labeling from CT using synthetic MR images
Can Zhao, Aaron Carass, Junghoon Lee, Yufan He, and Jerry L. Prince
· [MLMI-O-11] Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images
Danni Cheng, Manhua Liu
15:30 – 16:00 Coffee Break
16:00 – 17:30 Afternoon Session 2
Session Chair: TBD
· [MLMI-O-12] Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks
Nicha C. Dvornek, Pamela Ventola, Kevin A. Pelphrey, and James S. Duncan
· [MLMI-O-13] Collage CNN for Renal Cell Carcinoma Detection from CT
Mohammad Arafat Hussain, Alborz Amir-Khalili, Ghassan Hamarneh, and Rafeef Abugharbieh
· [MLMI-O-14] Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images
Kenji Suzuki, Junchi Liu, Amin Zarshenas, Toru Higaki, Wataru Fukumoto, and Kazuo Awai
· [MLMI-O-15] Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images
Zhen Yu, Xudong Jiang, Tianfu Wang, Baiying Lei
17:30 – 17:45 Closing remarks (Best paper(s) will be announced)